Seems pretty simple. When we create upper and lower boundaries to some score, people with lower scores have more space to overestimate and those with higher scores more space to underestimate, causing the perceived score to trend towards the mean.
I think there's both a component of numbers and psychology here. If the dispersion in perceived score caused by inaccuracy is wide enough to touch the bounds, it will force a trend towards the mean. This effect is possibly exacerbated by a tendency of perception to stray from "extremes", so subjects with a score near the edges will trend to the mean more strongly as they are unlikely to rate themselves the very best or very worst.
This seems pretty simple to correct, so I'm skeptical that nobody has done so yet in these experiments. If true, it's an equally interesting oversight as the Monty Hall problem. The basic premise is that the structure of an experiment will naturally nudge randomness in a particular direction, and we need to adjust for that in the analysis. Everyone who does this type of work should know this.
In a simplified experiment where we give people a 3 question quiz, those who got 2 questions right have one overestimation option, 3, and two underestimation options, 0 and 1. So it's very easy to adjust for autocorrelation by checking if a large group of 2-scorers underestimate more than twice as often as they overestimate. Then we see how their tendencies compare against 1-scorers and how they deviate from naturally overestimating more than twice as often as underestimating.
I haven't reviewed these types of papers, but if nobody made even that basic adjustment in their analysis, how many others have been missed in experiments like this?
I think there's both a component of numbers and psychology here. If the dispersion in perceived score caused by inaccuracy is wide enough to touch the bounds, it will force a trend towards the mean. This effect is possibly exacerbated by a tendency of perception to stray from "extremes", so subjects with a score near the edges will trend to the mean more strongly as they are unlikely to rate themselves the very best or very worst.